From 7881b93eab6a8dfcc948c64d4fe4a8e6785ef49f Mon Sep 17 00:00:00 2001 From: Abdullah mubeen <77073730+AbdullahMubeenAnwar@users.noreply.github.com> Date: Thu, 18 Jan 2024 17:16:59 +0500 Subject: [PATCH] removed code for connection to google drive (#14123) --- ...rk_NLP_RoBertaForTokenClassification.ipynb | 6249 +++++++++-------- 1 file changed, 3193 insertions(+), 3056 deletions(-) diff --git a/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_RoBertaForTokenClassification.ipynb b/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_RoBertaForTokenClassification.ipynb index 2c6ae4dca9a929..042a46f3bfe27c 100644 --- a/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_RoBertaForTokenClassification.ipynb +++ b/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_RoBertaForTokenClassification.ipynb @@ -1,3124 +1,3261 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_RoBertaForTokenClassification.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import ONNX RoBertaForTokenClassification models from HuggingFace 🤗 into Spark NLP 🚀\n", - "\n", - "Let's keep in mind a few things before we start 😊\n", - "\n", - "- ONNX support was introduced in `Spark NLP 5.0.0`, enabling high performance inference for models.\n", - "- `RoBertaForTokenClassification` is only available since in `Spark NLP 5.1.4` and after. So please make sure you have upgraded to the latest Spark NLP release\n", - "- You can import RoBERTa models trained/fine-tuned for token classification via `RobertaForTokenClassification` or `TFRobertaForTokenClassification`. These models are usually under `Token Classification` category and have `roberta` in their labels\n", - "- Reference: [TFRobertaForTokenClassification](https://huggingface.co/transformers/model_doc/roberta.html#tfrobertafortokenclassification)\n", - "- Some [example models](https://huggingface.co/models?filter=roberta&pipeline_tag=token-classification)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mounted at /content/drive\n" - ] - } - ], - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!cp drive/MyDrive/JSL/sparknlp/sparknlp.jar ." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Export and Save HuggingFace model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Let's install `transformers` package with the `onnx` extension and it's dependencies. You don't need `onnx` to be installed for Spark NLP, however, we need it to load and save models from HuggingFace.\n", - "- We lock `transformers` on version `4.29.1`. This doesn't mean it won't work with the future releases\n", - "- Albert uses SentencePiece, so we will have to install that as well" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m53.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m301.0/301.0 kB\u001b[0m \u001b[31m28.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", - " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", - " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m489.8/489.8 MB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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"\u001b[?25h Building wheel for optimum (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "tensorflow-datasets 4.9.3 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\n", - "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0m" - ] - } - ], - "source": [ - "!pip install -q --upgrade transformers[onnx]==4.29.1 optimum tensorflow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- HuggingFace has an extension called Optimum which offers specialized model inference, including ONNX. We can use this to import and export ONNX models with `from_pretrained` and `save_pretrained`.\n", - "- We'll use [philschmid/distilroberta-base-ner-wikiann-conll2003-3-class](https://huggingface.co/philschmid/distilroberta-base-ner-wikiann-conll2003-3-class) model from HuggingFace as an example and load it as a `ORTModelForSequenceClassification`, representing an ONNX model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "19403261179149178f0b54c0a125f198", - "version_major": 2, - "version_minor": 0 + "cell_type": "markdown", + "metadata": { + "id": "vctEEFUYk8Nu" }, - "text/plain": [ - "(…)nll2003-3-class/resolve/main/config.json: 0%| | 0.00/962 [00:00=3.20, but you have protobuf 3.19.6 which is incompatible.\n", + "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install -q --upgrade transformers[onnx]==4.29.1 optimum tensorflow" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "166671c87f7d48feafb05bb58c739600", - "version_major": 2, - "version_minor": 0 + "cell_type": "markdown", + "metadata": { + "id": "DfkYcjS3k8N5" }, - "text/plain": [ - "(…)2003-3-class/resolve/main/tokenizer.json: 0%| | 0.00/1.36M [00:00 False\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============= Diagnostic Run torch.onnx.export version 2.0.1+cu118 =============\n", + "verbose: False, log level: Level.ERROR\n", + "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n", + "\n" + ] + } + ], + "source": [ + "from optimum.onnxruntime import ORTModelForTokenClassification\n", + "import tensorflow as tf\n", + "\n", + "MODEL_NAME = 'philschmid/distilroberta-base-ner-wikiann-conll2003-3-class'\n", + "ONNX_MODEL = f\"onnx_models/{MODEL_NAME}\"\n", + "\n", + "ort_model = ORTModelForTokenClassification.from_pretrained(MODEL_NAME, export=True)\n", + "\n", + "# Save the ONNX model\n", + "ort_model.save_pretrained(ONNX_MODEL)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using the export variant default. Available variants are:\n", - "\t- default: The default ONNX variant.\n", - "Using framework PyTorch: 2.0.1+cu118\n", - "Overriding 1 configuration item(s)\n", - "\t- use_cache -> False\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YgpzkzZpk8N5" + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "# Read the vocab JSON file\n", + "with open('{}/vocab.json'.format(ONNX_MODEL), 'r') as json_file:\n", + " tokenizer = json.load(json_file)\n", + "\n", + "# let's save the vocab as txt file\n", + "with open('{}/vocab.txt'.format(ONNX_MODEL), 'w') as keys_file:\n", + " for item in tokenizer.keys():\n", + " keys_file.write(\"%s\\n\" % item)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "============= Diagnostic Run torch.onnx.export version 2.0.1+cu118 =============\n", - "verbose: False, log level: Level.ERROR\n", - "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n", - "\n" - ] - } - ], - "source": [ - "from optimum.onnxruntime import ORTModelForTokenClassification\n", - "import tensorflow as tf\n", - "\n", - "MODEL_NAME = 'philschmid/distilroberta-base-ner-wikiann-conll2003-3-class'\n", - "ONNX_MODEL = f\"onnx_models/{MODEL_NAME}\"\n", - "\n", - "ort_model = ORTModelForTokenClassification.from_pretrained(MODEL_NAME, export=True)\n", - "\n", - "# Save the ONNX model\n", - "ort_model.save_pretrained(ONNX_MODEL)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "# Read the vocab JSON file\n", - "with open('{}/vocab.json'.format(ONNX_MODEL), 'r') as json_file:\n", - " tokenizer = json.load(json_file)\n", - "\n", - "# let's save the vocab as txt file\n", - "with open('{}/vocab.txt'.format(ONNX_MODEL), 'w') as keys_file:\n", - " for item in tokenizer.keys():\n", - " keys_file.write(\"%s\\n\" % item)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's have a look inside these two directories and see what we are dealing with:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 321892\n", - "drwxr-xr-x 2 root root 4096 Oct 16 22:13 assets\n", - "-rw-r--r-- 1 root root 1034 Oct 16 22:10 config.json\n", - "-rw-r--r-- 1 root root 326278966 Oct 16 22:10 model.onnx\n", - "-rw-r--r-- 1 root root 280 Oct 16 22:10 special_tokens_map.json\n", - "-rw-r--r-- 1 root root 350 Oct 16 22:10 tokenizer_config.json\n", - "-rw-r--r-- 1 root root 2108715 Oct 16 22:10 tokenizer.json\n", - "-rw-r--r-- 1 root root 798293 Oct 16 22:10 vocab.json\n", - "-rw-r--r-- 1 root root 407065 Oct 16 22:18 vocab.txt\n" - ] - } - ], - "source": [ - "!ls -l {ONNX_MODEL}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!mkdir {ONNX_MODEL}/assets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- As you can see, we need to move `vocab.txt` and `merges.txt` from the tokenizer to `assets` folder which Spark NLP will look for\n", - "- We also need `labels` and their `ids` which is saved inside the model's config. We will save this inside `labels.txt`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# get label2id dictionary\n", - "labels = ort_model.config.id2label\n", - "# sort the dictionary based on the id\n", - "labels = [value for key,value in sorted(labels.items(), reverse=False)]\n", - "\n", - "with open(ONNX_MODEL + '/assets/labels.txt', 'w') as f:\n", - " f.write('\\n'.join(labels))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!mv {ONNX_MODEL}/vocab.txt {ONNX_MODEL}/assets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!mv {ONNX_MODEL}/merges.txt {ONNX_MODEL}/assets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Voila! We have our `vocab.txt` and `merges.txt` inside assets directory" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "onnx_models/philschmid/distilroberta-base-ner-wikiann-conll2003-3-class:\n", - "total 321892\n", - "drwxr-xr-x 2 root root 4096 Oct 16 22:18 assets\n", - "-rw-r--r-- 1 root root 1034 Oct 16 22:10 config.json\n", - "-rw-r--r-- 1 root root 326278966 Oct 16 22:10 model.onnx\n", - "-rw-r--r-- 1 root root 280 Oct 16 22:10 special_tokens_map.json\n", - "-rw-r--r-- 1 root root 350 Oct 16 22:10 tokenizer_config.json\n", - "-rw-r--r-- 1 root root 2108715 Oct 16 22:10 tokenizer.json\n", - "-rw-r--r-- 1 root root 798293 Oct 16 22:10 vocab.json\n", - "-rw-r--r-- 1 root root 407065 Oct 16 22:18 vocab.txt\n", - "\n", - "onnx_models/philschmid/distilroberta-base-ner-wikiann-conll2003-3-class/assets:\n", - "total 852\n", - "-rw-r--r-- 1 root root 37 Oct 16 22:18 labels.txt\n", - "-rw-r--r-- 1 root root 456318 Oct 16 22:10 merges.txt\n", - "-rw-r--r-- 1 root root 407065 Oct 16 22:12 vocab.txt\n" - ] - } - ], - "source": [ - "!ls -lR {ONNX_MODEL}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import and Save RoBertaForTokenClassification in Spark NLP\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Let's install and setup Spark NLP in Google Colab\n", - "- This part is pretty easy via our simple script" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Installing PySpark 3.2.3 and Spark NLP 5.1.3\n", - "setup Colab for PySpark 3.2.3 and Spark NLP 5.1.3\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m281.5/281.5 MB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m537.5/537.5 kB\u001b[0m \u001b[31m33.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 kB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n" - ] - } - ], - "source": [ - "! wget -q http://setup.johnsnowlabs.com/colab.sh -O - | bash" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start Spark with Spark NLP included via our simple `start()` function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Apache Spark version: 3.2.3\n" - ] - } - ], - "source": [ - "import sparknlp\n", - "# let's start Spark with Spark NLP\n", - "spark = sparknlp.start()\n", - "\n", - "print(\"Apache Spark version: {}\".format(spark.version))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Let's use `loadSavedModel` functon in `RoBertaForTokenClassification` which allows us to load TensorFlow model in SavedModel format\n", - "- Most params can be set later when you are loading this model in `RoBertaForTokenClassification` in runtime like `setMaxSentenceLength`, so don't worry what you are setting them now\n", - "- `loadSavedModel` accepts two params, first is the path to the TF SavedModel. The second is the SparkSession that is `spark` variable we previously started via `sparknlp.start()`\n", - "- NOTE: `loadSavedModel` accepts local paths in addition to distributed file systems such as `HDFS`, `S3`, `DBFS`, etc. This feature was introduced in Spark NLP 4.2.2 release. Keep in mind the best and recommended way to move/share/reuse Spark NLP models is to use `write.save` so you can use `.load()` from any file systems natively.st and recommended way to move/share/reuse Spark NLP models is to use `write.save` so you can use `.load()` from any file systems natively." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sparknlp.annotator import *\n", - "from sparknlp.base import *\n", - "\n", - "tokenClassifier = RoBertaForTokenClassification\\\n", - " .loadSavedModel(ONNX_MODEL, spark)\\\n", - " .setInputCols([\"document\",'token'])\\\n", - " .setOutputCol(\"ner\")\\\n", - " .setCaseSensitive(True)\\\n", - " .setMaxSentenceLength(128)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Let's save it on disk so it is easier to be moved around and also be used later via `.load` function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tokenClassifier.write().overwrite().save(\"./{}_spark_nlp_onnx\".format(ONNX_MODEL))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's clean up stuff we don't need anymore" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!rm -rf {ONNX_MODEL}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Awesome 😎 !\n", - "\n", - "This is your RoBertaForTokenClassification model from HuggingFace 🤗 loaded and saved by Spark NLP 🚀" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 318696\n", - "drwxr-xr-x 5 root root 4096 Oct 16 22:21 fields\n", - "drwxr-xr-x 2 root root 4096 Oct 16 22:21 metadata\n", - "-rw-r--r-- 1 root root 326328924 Oct 16 22:21 roberta_classification_onnx\n" - ] - } - ], - "source": [ - "! ls -l {ONNX_MODEL}_spark_nlp_onnx" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's see how we can use it on other machines, clusters, or any place you wish to use your new and shiny RoBertaForTokenClassification model 😊" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tokenClassifier_loaded = RoBertaForTokenClassification.load(\"./{}_spark_nlp_onnx\".format(ONNX_MODEL))\\\n", - " .setInputCols([\"document\",'token'])\\\n", - " .setOutputCol(\"ner\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can see what labels were used to train this model via `getClasses` function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['B-LOC', 'I-ORG', 'I-LOC', 'I-PER', 'B-ORG', 'O', 'B-PER']" + "cell_type": "markdown", + "metadata": { + "id": "8k9xqg-sk8N6" + }, + "source": [ + "Let's have a look inside these two directories and see what we are dealing with:" ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# .getClasses was introduced in spark-nlp==3.4.0\n", - "tokenClassifier_loaded.getClasses()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is how you can use your loaded classifier model in Spark NLP 🚀 pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "+--------------------+--------------------+\n", - "| text| result|\n", - "+--------------------+--------------------+\n", - "|My name is Clara ...|[O, O, O, B-PER, ...|\n", - "|My name is Clara ...|[O, O, O, B-PER, ...|\n", - "+--------------------+--------------------+\n", - "\n" - ] - } - ], - "source": [ - "document_assembler = DocumentAssembler() \\\n", - " .setInputCol('text') \\\n", - " .setOutputCol('document')\n", - "\n", - "tokenizer = Tokenizer() \\\n", - " .setInputCols(['document']) \\\n", - " .setOutputCol('token')\n", - "\n", - "pipeline = Pipeline(stages=[\n", - " document_assembler,\n", - " tokenizer,\n", - " tokenClassifier_loaded\n", - "])\n", - "\n", - "# couple of simple examples\n", - "example = spark.createDataFrame([[\"My name is Clara and I live in Berkeley, California.\"], ['My name is Clara and I live in Berkeley, California.']]).toDF(\"text\")\n", - "\n", - "result = pipeline.fit(example).transform(example)\n", - "\n", - "# result is a DataFrame\n", - "result.select(\"text\", \"ner.result\").show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it! You can now go wild and use hundreds of `RoBertaForTokenClassification` models from HuggingFace 🤗 in Spark NLP 🚀\n" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "00d4770b7983470192967410038d0068": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c33367067b5c41529e4cb8301bb4631b", - "IPY_MODEL_f56039a6fb3f4dc7913ea06536e476c3", - "IPY_MODEL_f4f066292c894698a145d97645ef0852" + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mh74NCVMk8N6", + "outputId": "a3f534a5-2e80-4ba4-d3f0-846defda6932" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 321892\n", + "drwxr-xr-x 2 root root 4096 Oct 16 22:13 assets\n", + "-rw-r--r-- 1 root root 1034 Oct 16 22:10 config.json\n", + "-rw-r--r-- 1 root root 326278966 Oct 16 22:10 model.onnx\n", + "-rw-r--r-- 1 root root 280 Oct 16 22:10 special_tokens_map.json\n", + "-rw-r--r-- 1 root root 350 Oct 16 22:10 tokenizer_config.json\n", + "-rw-r--r-- 1 root root 2108715 Oct 16 22:10 tokenizer.json\n", + "-rw-r--r-- 1 root root 798293 Oct 16 22:10 vocab.json\n", + "-rw-r--r-- 1 root root 407065 Oct 16 22:18 vocab.txt\n" + ] + } ], - "layout": "IPY_MODEL_74cda4b89a124b009c187cb98a04899d" - } - }, - "025eda03fbad4dd18d7dae72aedd0106": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } + "source": [ + "!ls -l {ONNX_MODEL}" + ] }, - "050dbc230ffa47e1a8b293f622b4ea57": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_fb53f3bf55664c4e9aa685809d9b550f", - "max": 326181207, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_7d587ac5d3ee4a89a99bc5c0b8044669", - "value": 326181207 - } + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i24WdH62k8N7" + }, + "outputs": [], + "source": [ + "!mkdir {ONNX_MODEL}/assets" + ] }, - "0993a78aca3348468b8615d096466b80": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + { + "cell_type": "markdown", + "metadata": { + "id": "R7clJnf-k8N7" + }, + "source": [ + "- As you can see, we need to move `vocab.txt` and `merges.txt` from the tokenizer to `assets` folder which Spark NLP will look for\n", + "- We also need `labels` and their `ids` which is saved inside the model's config. We will save this inside `labels.txt`" + ] }, - "0b89fef36cfa4301a27a58e6a1dec354": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Acu1x8BQk8N7" + }, + "outputs": [], + "source": [ + "# get label2id dictionary\n", + "labels = ort_model.config.id2label\n", + "# sort the dictionary based on the id\n", + "labels = [value for key,value in sorted(labels.items(), reverse=False)]\n", + "\n", + "with open(ONNX_MODEL + '/assets/labels.txt', 'w') as f:\n", + " f.write('\\n'.join(labels))" + ] }, - "0fc0a55a8d234a17a7d725a93c45fd50": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7Jm6IFTSk8N8" + }, + "outputs": [], + "source": [ + "!mv {ONNX_MODEL}/vocab.txt {ONNX_MODEL}/assets" + ] }, - "12eee2449390429192df0e0394598062": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PfDg1SyDk8N8" + }, + "outputs": [], + "source": [ + "!mv {ONNX_MODEL}/merges.txt {ONNX_MODEL}/assets" + ] }, - "1383a4cde8674b039c59c15a63901461": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_0fc0a55a8d234a17a7d725a93c45fd50", - "placeholder": "​", - "style": "IPY_MODEL_b7201dc4f9584e1c97488425a766c4c6", - "value": "(…)2003-3-class/resolve/main/tokenizer.json: 100%" - } + { + "cell_type": "markdown", + "metadata": { + "id": "m4cXGPOEk8N8" + }, + "source": [ + "Voila! We have our `vocab.txt` and `merges.txt` inside assets directory" + ] }, - "166671c87f7d48feafb05bb58c739600": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1383a4cde8674b039c59c15a63901461", - "IPY_MODEL_3de9ee6582f1423598931cea294c532c", - "IPY_MODEL_ac0bec7637084a0e8e51231de626f69e" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dWb6ZEtXk8N8", + "outputId": "7f110ef4-8cb5-48e1-925d-338ae57c5046" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "onnx_models/philschmid/distilroberta-base-ner-wikiann-conll2003-3-class:\n", + "total 321892\n", + "drwxr-xr-x 2 root root 4096 Oct 16 22:18 assets\n", + "-rw-r--r-- 1 root root 1034 Oct 16 22:10 config.json\n", + "-rw-r--r-- 1 root root 326278966 Oct 16 22:10 model.onnx\n", + "-rw-r--r-- 1 root root 280 Oct 16 22:10 special_tokens_map.json\n", + "-rw-r--r-- 1 root root 350 Oct 16 22:10 tokenizer_config.json\n", + "-rw-r--r-- 1 root root 2108715 Oct 16 22:10 tokenizer.json\n", + "-rw-r--r-- 1 root root 798293 Oct 16 22:10 vocab.json\n", + "-rw-r--r-- 1 root root 407065 Oct 16 22:18 vocab.txt\n", + "\n", + "onnx_models/philschmid/distilroberta-base-ner-wikiann-conll2003-3-class/assets:\n", + "total 852\n", + "-rw-r--r-- 1 root root 37 Oct 16 22:18 labels.txt\n", + "-rw-r--r-- 1 root root 456318 Oct 16 22:10 merges.txt\n", + "-rw-r--r-- 1 root root 407065 Oct 16 22:12 vocab.txt\n" + ] + } ], - "layout": "IPY_MODEL_d72f34d844b542b0a4e1ec0264880cab" - } - }, - "1689463b2a3d4b39bb427733c160287a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } + "source": [ + "!ls -lR {ONNX_MODEL}" + ] }, - "1898befd7f36447ea5194e2c68d00c31": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_da56089370b6403fa52b9787b84ad86d", - "placeholder": "​", - "style": "IPY_MODEL_b8ed253331fe4d4e9b7a10dd282ea172", - "value": " 326M/326M [00:06<00:00, 37.1MB/s]" - } + { + "cell_type": "markdown", + "metadata": { + "id": "W9-Fowe_k8N9" + }, + "source": [ + "## Import and Save RoBertaForTokenClassification in Spark NLP\n" + ] }, - "191f55fc572b4f5a9b41e0c0dbd20414": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_2db07d4ad6ff49b5b5ce76ea60c655fe", - "placeholder": "​", - "style": "IPY_MODEL_528de7c76ae84ccfb4614faddf133cfb", - "value": " 962/962 [00:00<00:00, 26.2kB/s]" - } + { + "cell_type": "markdown", + "metadata": { + "id": "h0II8wYvk8N9" + }, + "source": [ + "- Let's install and setup Spark NLP in Google Colab\n", + "- This part is pretty easy via our simple script" + ] }, - "19403261179149178f0b54c0a125f198": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c1ac31ed4ded444586913047df105d63", - "IPY_MODEL_1ccb91d2654d47d7aa883c016a8b4e49", - "IPY_MODEL_191f55fc572b4f5a9b41e0c0dbd20414" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gsYqnf_gk8N9", + "outputId": "9dfa476f-0c5c-48ae-daf6-0b7fb3ef4bcb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Installing PySpark 3.2.3 and Spark NLP 5.1.3\n", + "setup Colab for PySpark 3.2.3 and Spark NLP 5.1.3\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m281.5/281.5 MB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m537.5/537.5 kB\u001b[0m \u001b[31m33.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 kB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n" + ] + } ], - 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"overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + "source": [ + "import sparknlp\n", + "# let's start Spark with Spark NLP\n", + "spark = sparknlp.start()\n", + "\n", + "print(\"Apache Spark version: {}\".format(spark.version))" + ] }, - "885a765e32834db28e6a6aa47a853a8f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } + { + "cell_type": "markdown", + "metadata": { + "id": "pj76mzEuk8N-" + }, + "source": [ + "- Let's use `loadSavedModel` functon in `RoBertaForTokenClassification` which allows us to load TensorFlow model in SavedModel format\n", + "- Most params can be set later when you are loading this model in `RoBertaForTokenClassification` in runtime like `setMaxSentenceLength`, so don't worry what you are setting them now\n", + "- `loadSavedModel` accepts two params, first is the path to the TF SavedModel. The second is the SparkSession that is `spark` variable we previously started via `sparknlp.start()`\n", + "- NOTE: `loadSavedModel` accepts local paths in addition to distributed file systems such as `HDFS`, `S3`, `DBFS`, etc. This feature was introduced in Spark NLP 4.2.2 release. Keep in mind the best and recommended way to move/share/reuse Spark NLP models is to use `write.save` so you can use `.load()` from any file systems natively.st and recommended way to move/share/reuse Spark NLP models is to use `write.save` so you can use `.load()` from any file systems natively." + ] }, - "8caea9c1009646e9839e9e410f1006b8": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wX3vfOybk8N-" + }, + "outputs": [], + "source": [ + "from sparknlp.annotator import *\n", + "from sparknlp.base import *\n", + "\n", + "tokenClassifier = RoBertaForTokenClassification\\\n", + " .loadSavedModel(ONNX_MODEL, spark)\\\n", + " .setInputCols([\"document\",'token'])\\\n", + " .setOutputCol(\"ner\")\\\n", + " .setCaseSensitive(True)\\\n", + " .setMaxSentenceLength(128)" + ] }, - 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"placeholder": "​", - "style": "IPY_MODEL_ec26975de7f3493795c3cdf5a471a59d", - "value": " 293/293 [00:00<00:00, 15.7kB/s]" - } + "source": [ + "! ls -l {ONNX_MODEL}_spark_nlp_onnx" + ] }, - "e542527c11944d088846505d08c52806": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_8caea9c1009646e9839e9e410f1006b8", - "placeholder": "​", - "style": "IPY_MODEL_afe9cc266e03429b84d094ab1cb29a97", - "value": "(…)class/resolve/main/tokenizer_config.json: 100%" - } + { + "cell_type": "markdown", + "metadata": { + "id": "v4phtEi-k8N_" + }, + "source": [ + "Now let's see how we can use it on other machines, clusters, or any place you wish to use your new and shiny RoBertaForTokenClassification model 😊" + ] }, - 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"_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + "source": [ + "# .getClasses was introduced in spark-nlp==3.4.0\n", + "tokenClassifier_loaded.getClasses()" + ] }, - 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You can now go wild and use hundreds of `RoBertaForTokenClassification` models from HuggingFace 🤗 in Spark NLP 🚀\n" + ] } - } - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "00d4770b7983470192967410038d0068": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": 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